Bottom Line:
As expected, injection of current noise via the recording pipette induces shifts in neuronal gain that are dependent on the amplitude of current noise, such that larger shifts in gain are observed in response to larger amplitude noise injections.In contrast, when the cortical feedback network was activated, only multiplicative gain changes were observed.These network activation-dependent changes were associated with reductions in the slow afterhyperpolarization (sAHP), and were mediated at least in part, by T-type calcium channels.

Affiliation: Sydney Medical School, School of Medical Sciences and Bosch Institute, The University of Sydney, New South Wales, Australia.

ABSTRACTThe output of individual neurons is dependent on both synaptic and intrinsic membrane properties. While it is clear that the response of an individual neuron can be facilitated or inhibited based on the summation of its constituent synaptic inputs, it is not clear whether subthreshold activity, (e.g. synaptic "noise"--fluctuations that do not change the mean membrane potential) also serve a function in the control of neuronal output. Here we studied this by making whole-cell patch-clamp recordings from 29 mouse thalamocortical relay (TC) neurons. For each neuron we measured neuronal gain in response to either injection of current noise, or activation of the metabotropic glutamate receptor-mediated cortical feedback network (synaptic noise). As expected, injection of current noise via the recording pipette induces shifts in neuronal gain that are dependent on the amplitude of current noise, such that larger shifts in gain are observed in response to larger amplitude noise injections. Importantly we show that shifts in neuronal gain are also dependent on the intrinsic sensitivity of the neuron tested, such that the gain of intrinsically sensitive neurons is attenuated divisively in response to current noise, while the gain of insensitive neurons is facilitated multiplicatively by injection of current noise- effectively normalizing the output of the dLGN as a whole. In contrast, when the cortical feedback network was activated, only multiplicative gain changes were observed. These network activation-dependent changes were associated with reductions in the slow afterhyperpolarization (sAHP), and were mediated at least in part, by T-type calcium channels. Together, this suggests that TC neurons have the machinery necessary to compute multiple output solutions to a given set of stimuli depending on the current level of network stimulation.

pone-0057961-g001: Schematic of the mouse dorsal lateral geniculate nucleus (dLGN) and representative noise stimuli.A. The dLGN is shown in relation the hippocampus (CA3 and CA1), ventral lateral geniculate nucleus (vLGN), lateral posterior nucleus (LP), posterior nucleus (PO), and the medial portion of the posterior nucleus (VPM) in a coronal plane (2.06 mm caudal to Bregma, left hemisphere). Inset shows the map of recording sites within the dLGN. Note that cells were recorded throughout the dorsoventral, and mediolateral extent of the LGN (Plates 45–51 in Paxinos and Watson, 2008. On the bottom right is a photo of a representative TC neuron. B. The response of a cell to a noisy current stimulus with a mean current of 0 pA. The value σ12.5 for current noise of different standard deviations (n) was calculated as the standard deviation of the recorded membrane potential. The noise levels presented throughout are the average of the standard deviation (caused by this stimulus for each n) across all cells tested.

Mentions:
After incubation slices were transferred to a small glass-bottom recording chamber and secured by a weighted nylon net. The chamber was continually perfused (5–6 bath volumes/min) with oxygenized ACSF at 32 ± 1°C. Slices were viewed using a fixed-stage microscope (Olympus BX-51WI, Tokyo, Japan) at low power (10x) to identify the dLGN. Thalamic neurons were visually identified using near infra-red differential interference contrast optics and a high power (40x) water-immersion lens. Micropipettes were pulled from thin-walled borosilicate glass tubing (1.5 mm OD, Warner Instruments, Hamden, Connecticut) using a micropipette puller (Narishige, Tokyo, Japan). Pipettes were filled with a potassium-based internal electrode solution containing (in mM): 70 potassium gluconate, 70 KCl, 2 NaCl, 10 HEPES, 4 EGTA, 4 Mg2-ATP, 0.3 Na3-GTP. The pH was adjusted using KOH to give a final pH of 7.3 and an osmolarity of 290 mOsmol [27], [30]. Lucifer yellow (0.5 mg/mL, Invitrogen, Eugene, Oregon) was included in the internal solution to allow for post-recording morphological analysis of individual neurons and mapping of recording sites. Recording pipettes (final resistance of 4–7 MΩ) were positioned in the recording chamber using a motorised micromanipulator (Sutter, Nuslock City, Germany). Voltage data was corrected for a measured junction potential of -6 mV, and fast and slow capacitance was uncompensated. Targeted recordings were made throughout the anatomical extent of the dLGN to sample from the largest possible cell population (Figure 1).

pone-0057961-g001: Schematic of the mouse dorsal lateral geniculate nucleus (dLGN) and representative noise stimuli.A. The dLGN is shown in relation the hippocampus (CA3 and CA1), ventral lateral geniculate nucleus (vLGN), lateral posterior nucleus (LP), posterior nucleus (PO), and the medial portion of the posterior nucleus (VPM) in a coronal plane (2.06 mm caudal to Bregma, left hemisphere). Inset shows the map of recording sites within the dLGN. Note that cells were recorded throughout the dorsoventral, and mediolateral extent of the LGN (Plates 45–51 in Paxinos and Watson, 2008. On the bottom right is a photo of a representative TC neuron. B. The response of a cell to a noisy current stimulus with a mean current of 0 pA. The value σ12.5 for current noise of different standard deviations (n) was calculated as the standard deviation of the recorded membrane potential. The noise levels presented throughout are the average of the standard deviation (caused by this stimulus for each n) across all cells tested.

Mentions:
After incubation slices were transferred to a small glass-bottom recording chamber and secured by a weighted nylon net. The chamber was continually perfused (5–6 bath volumes/min) with oxygenized ACSF at 32 ± 1°C. Slices were viewed using a fixed-stage microscope (Olympus BX-51WI, Tokyo, Japan) at low power (10x) to identify the dLGN. Thalamic neurons were visually identified using near infra-red differential interference contrast optics and a high power (40x) water-immersion lens. Micropipettes were pulled from thin-walled borosilicate glass tubing (1.5 mm OD, Warner Instruments, Hamden, Connecticut) using a micropipette puller (Narishige, Tokyo, Japan). Pipettes were filled with a potassium-based internal electrode solution containing (in mM): 70 potassium gluconate, 70 KCl, 2 NaCl, 10 HEPES, 4 EGTA, 4 Mg2-ATP, 0.3 Na3-GTP. The pH was adjusted using KOH to give a final pH of 7.3 and an osmolarity of 290 mOsmol [27], [30]. Lucifer yellow (0.5 mg/mL, Invitrogen, Eugene, Oregon) was included in the internal solution to allow for post-recording morphological analysis of individual neurons and mapping of recording sites. Recording pipettes (final resistance of 4–7 MΩ) were positioned in the recording chamber using a motorised micromanipulator (Sutter, Nuslock City, Germany). Voltage data was corrected for a measured junction potential of -6 mV, and fast and slow capacitance was uncompensated. Targeted recordings were made throughout the anatomical extent of the dLGN to sample from the largest possible cell population (Figure 1).

Bottom Line:
As expected, injection of current noise via the recording pipette induces shifts in neuronal gain that are dependent on the amplitude of current noise, such that larger shifts in gain are observed in response to larger amplitude noise injections.In contrast, when the cortical feedback network was activated, only multiplicative gain changes were observed.These network activation-dependent changes were associated with reductions in the slow afterhyperpolarization (sAHP), and were mediated at least in part, by T-type calcium channels.

Affiliation:
Sydney Medical School, School of Medical Sciences and Bosch Institute, The University of Sydney, New South Wales, Australia.

ABSTRACTThe output of individual neurons is dependent on both synaptic and intrinsic membrane properties. While it is clear that the response of an individual neuron can be facilitated or inhibited based on the summation of its constituent synaptic inputs, it is not clear whether subthreshold activity, (e.g. synaptic "noise"--fluctuations that do not change the mean membrane potential) also serve a function in the control of neuronal output. Here we studied this by making whole-cell patch-clamp recordings from 29 mouse thalamocortical relay (TC) neurons. For each neuron we measured neuronal gain in response to either injection of current noise, or activation of the metabotropic glutamate receptor-mediated cortical feedback network (synaptic noise). As expected, injection of current noise via the recording pipette induces shifts in neuronal gain that are dependent on the amplitude of current noise, such that larger shifts in gain are observed in response to larger amplitude noise injections. Importantly we show that shifts in neuronal gain are also dependent on the intrinsic sensitivity of the neuron tested, such that the gain of intrinsically sensitive neurons is attenuated divisively in response to current noise, while the gain of insensitive neurons is facilitated multiplicatively by injection of current noise- effectively normalizing the output of the dLGN as a whole. In contrast, when the cortical feedback network was activated, only multiplicative gain changes were observed. These network activation-dependent changes were associated with reductions in the slow afterhyperpolarization (sAHP), and were mediated at least in part, by T-type calcium channels. Together, this suggests that TC neurons have the machinery necessary to compute multiple output solutions to a given set of stimuli depending on the current level of network stimulation.